A system for monitoring the state of charge of a battery, utilizing a neural network, is already known from the publication entitled "NEURAL NETWORK, A PROPER APPROACH TO THE ENERGY MANAGEMENT PROBLEM", by MARCUS STOLL IN "10TH EUROPEAN PHOTOVOLTAIC SOLAR ENERGY CONFERENCE", 8-10 Apr. 1991, LISBON. PORTUGAL, pages 427-430.
The cited publication describes the use of a neural network for undertaking the task of estimating the state of charge (SOC) of a lead-acid battery in a recharging system (RES). According to the cited document, determining the state of charge (SOC) is an important task that is to be carried out to monitor the energy level of a battery. More particularly, the estimation of the state of charge makes it possible to plan the use of the renewable energy, to optimize the conditions of use of a host device, and to make decisions concerning the various periods of the charging/discharging cycles of the battery.
A neural network is involved in estimating the state of charge (SOC) via a data base. To reduce the cost, the neural network is involved in only a small part of the discharging domain of the battery. As the discharge current is very small during most of the time, the involvement of the neural network lies in this domain.
In the learning period of the neural network a data base is used including the discharge current, the discharge voltage and the state of charge under standard conditions of use, that is to say, at a fixed temperature of 20.degree. C. and with a fixed current. In addition, this data base may include information relating to discharging cycles and to what degree the discharge has taken place and relating to the average temperature of the battery. The various batches of these data, which form input vectors, are applied to the neural network to inform the network of the discharging behavior of the batteries. The neural network is arranged for a suitable representation of the behavior of the battery.
In the classification period of the neural network, only the discharge current and voltage are applied thereto and it produces on its output the corresponding state of charge of the battery.
A problem which results from the use of the known system is that this system is unable to predict forthwith the lapse of time that is left before a critical discharge voltage threshold is reached.
Another problem which results from the use of the known system is that the data corresponding to the number of previous charging/discharging cycles and to the degree of discharge in these cycles, cannot be correctly taken into account. Indeed, these data are highly variable as a function of the actual use that is made of the battery during operation, and largely influence the real state of charge present in the battery at a given instant of a discharging cycle, whereas in the known system of the cited document the weights of the neural network are ultimately fixed from the end of the learning period onwards.